Source Code: https://goo.gl/Q3Gt5m References: https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/ http://www.inf.ed.ac.uk/teaching/courses/inf2b/learnnotes/inf2b-learn-note07-2up.pdf https://data.world/datasets/twitter In this video I explain how you can use machine learning algorithms on text data, using the example of twitter sentiment analysis. I have got the dataset of trump related tweets. It is there in the above mentioned website. This code looks though all the data and then figures out if a tweet is a positive tweet or a negative tweet. After the classification(positive sentiment/negative sentiment) it saves the data in a file. Code work offers you a variety of educational videos to enhance your programming skills. At times I create videos without prior preparations so that I can show you the mistakes I am making so that you don't repeat those mistakes yourself. It's humanly to make errors, so if you find some errors in my videos please leave a comment below and I will address them or you can email me at [email protected] stating the problem. I shall try to address all of you . Finally please hit hike . . . and do subscribe so that you get to know at once when some video is being released. Happy coding . .. Epic pen: http://epic-pen.com Screen Recorder: https://obsproject.com/ Facebook https://www.facebook.com/Coding-algorithms-datastructure-Codeworks-1520910904866937/ google plus https://plus.google.com/118085047343771284166 My Website: http://www.the-tinker-project.co.in/blog/
Views: 4430 code works
Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics. Deep Learning TV on Facebook: https://www.facebook.com/DeepLearningTV/ Twitter: https://twitter.com/deeplearningtv Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency. Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words. One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word. The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector. Two popular tools: Word2Vec: https://code.google.com/archive/p/word2vec/ Glove: http://nlp.stanford.edu/projects/glove/ Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse. Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language. Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis: “He turned around a team otherwise known for overall bad temperament” In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of the sentence and properly label it as positive. Credits Nickey Pickorita (YouTube art) - https://www.upwork.com/freelancers/~0147b8991909b20fca Isabel Descutner (Voice) - https://www.youtube.com/user/IsabelDescutner Dan Partynski (Copy Editing) - https://www.linkedin.com/in/danielpartynski Marek Scibior (Prezi creator, Illustrator) - http://brawuroweprezentacje.pl/ Jagannath Rajagopal (Creator, Producer and Director) - https://ca.linkedin.com/in/jagannathrajagopal
Views: 43523 DeepLearning.TV
This episode of Fresh Machine Learning is all Tone Analysis. Tone analysis consists of not just analyzing sentiment (positive or negative), but also analyzing emotions as well as writing style. There are a lot of dimensions to tone, and in this episode I talk about what I consider to be 3 seminal papers in this field. At the end of the episode, we use IBM’s Watson Tone Analyzer API to build our own tone analysis web app. The demo code for this video can be found here: https://github.com/llSourcell/Tone-Analyzer I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ I introduce three papers in this video Convolutional neural networks for sentence classification: http://emnlp2014.org/papers/pdf/EMNLP2014181.pdf Text categorization using LSTM for region embeddings: http://arxiv.org/pdf/1602.02373v2.pdf Hierarchical attention networks for document classification: https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf More info about the IBM Watson Tone Analyzer API: http://www.ibm.com/watson/developercloud/tone-analyzer.html Some great notes, slides, and practice problems for NLP: http://cs224d.stanford.edu/syllabus.html Live demo of the Watson Tone Analyzer: https://tone-analyzer-demo.mybluemix.net/ Really great long-form page talking about text classification http://www.nltk.org/book/ch06.html I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now. I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Much more to come so please subscribe, like, and comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 14688 Siraj Raval
This video highlights the ChartExpo sentiment analysis visualizations used in the PolyVista Interactive PDFs for Text Analysis.
Views: 59 PolyVista
Download the PDF to keep as reference http://theexcelclub.com/sentiment-analysis-with-power-bi-and-microsoft-cognitive-services/ FREE Power BI course - Power BI - The Ultimate Orientation http://theexcelclub.com/free-excel-training/ Or on Udemy https://www.udemy.com/power-bi-the-ultimate-orientation Or on Android App https://play.google.com/store/apps/details?id=com.PBI.trainigapp Carry out a sentiment analysis like the big brand...only free with Power BI and Microsoft Cognitive Services. this video will cover Obtain a Text Analytics API Key from Microsoft Cognitive Services Power BI – Setting up the Text Data Setting up the Parameter in Power BI Setting up the Custom function Query(with code to copy) Grouping the text Running the sentiment analysis by calling the custom function. Extracting the sentiment from the returned Json file. Sign up to our newsletter http://theexcelclub.com/newsletter/ Watch more Power BI videos https://www.youtube.com/playlist?list=PLJ35EHVzCuiEsQ-68y0tdnaU9hCqjJ5Dh Watch Excel Videos https://www.youtube.com/playlist?list=PLJ35EHVzCuiFFpjWeK7CE3AEXy_IRZp4y Join the online Excel and PowerBI community https://plus.google.com/u/0/communities/110804786414261269900
Views: 6648 Paula Guilfoyle
This tutorial shows how to conduct text sentiment analysis in R. We'll be pulling tweets from the Twitter web API, comparing each word to positive and negative word bank, and then using a basic algorithm to determine the overall sentiment. We'll then create several charts and graphs to organize the data. Updated code: http://silviaplanella.wordpress.com/2014/12/31/sentiment-analysis-twitter-and-r/ https://github.com/mjhea0/twitter-sentiment-analysis https://gist.github.com/mjhea0/5497065 TwitteR docs - http://cran.r-project.org/web/packages/twitteR/twitteR.pdf
Views: 64324 Michael Herman
We show how to build a machine learning document classification system from scratch in less than 30 minutes using R. We use a text mining approach to identify the speaker of unmarked presidential campaign speeches. Applications in brand management, auditing, fraud detection, electronic medical records, and more.
Views: 163432 Timothy DAuria
It’s easy to get lost in a lot of text-based data. NVivo is qualitative data analysis software that provides structure to text, helping you quickly unlock insights and make something beautiful to share. http://www.qsrinternational.com
Views: 128918 NVivo by QSR
Watch this video to see how IBM Watson Analytics for Social Media (WASM) is used to analyze data, topics, cluster & sentimentalist analysis. Understand churn patterns and watch influential authors with their sentiments and feedback.
Views: 39 Cresco International
5th Annual Wolfram Data Summit 2014 Ronen Feldman, Chief Scientist, Digital Trowel Sentiment analysis is defined as the task of finding the opinions of authors about specific entities. The decision making process of people is affected by the opinions formed by thought leaders and ordinary people. In this talk, we mostly focus on analyzing subjective sentences. However, we refer to the usage of objective sentences when we describe a sentiment application for stock picking. For the latest information, please visit: http://www.wolfram.com
Views: 4840 Wolfram
Views: 263 Adrien GC
A very basic example: convert unstructured data from text files to structured analyzable format.
Views: 12041 Stat Pharm
NLP Tutorial with TextBlob & Python -Sentiment Analysis In this tutorial we will be performing basic sentiment analysis with TextBlob Tutorial Here: Github:https://bit.ly/2I09ucw
Views: 616 J-Secur1ty
This tutorial video covers how to do real-time analysis alongside your streaming Twitter API v1.1 feed. In this case, for example, we use the Sentdex Sentiment Analysis API, http://sentdex.com/sentiment-analysis-api/, though you can use ANY API like this, or just your own custom function too. If you don't already have a twitter stream set up, here is some sample code and tutorial video for it: http://sentdex.com/sentiment-analysisbig-data-and-python-tutorials-algorithmic-trading/how-to-use-the-twitter-api-1-1-to-stream-tweets-in-python/ Sentdex.com Facebook.com/sentdex Twitter.com/sentdex
Views: 70623 sentdex
Word embeddings are one of the coolest things you can do with Machine Learning right now. Try the web app: https://embeddings.macheads101.com Word2vec paper: https://arxiv.org/abs/1301.3781 GloVe paper: https://nlp.stanford.edu/pubs/glove.pdf GloVe webpage: https://nlp.stanford.edu/projects/glove/ Other resources: http://www.aclweb.org/anthology/Q15-1016 https://en.wikipedia.org/wiki/Word_embedding
Views: 50951 macheads101
The slides are here: https://github.com/ml-rn/slides/blob/master/nn_nlp/presentation.pdf Sadly the recording has not worked from the beginning, but it is mostly the introduction that is missing.
Views: 223 Machine-Learning Rhein-Neckar
sentiment analysis of tweets using bid data tool called pig. Have you subscribed the channel for more update. please share and subscribe. For any queries and suggestion connect with me or follow me at: Facebook: https://www.facebook.com/Andani.tec/ Mail:[email protected] what's up me:8951817903 We provide Big Data Projects for College Students: contact: https://docs.google.com/forms/d/1FknqMvButSEQ62rrpeB_6jB7YYEt-JU06YRyi4KtzFg/viewform?edit_requested=true we even provide: 1.Internship 2.tranning 3.Start-up-company website creation For More Inforamtion visit our site: https://apksolutions9.wixsite.com/fine-tech
Views: 1293 Something BIG
Text Mining with R. Import a single document into R.
Views: 18592 Jalayer Academy
This is a demonstration based session which will show how to use a HDInsight (Apache Hadoop exposed as an Azure Service) cluster to do sentiment analysis from live Twitter feeds on a specific keyword or brand. Sentiment analysis is parsing unstructured data that represents opinions, emotions, and attitudes contained in sources such as social media posts, blogs, online product reviews, and customer support interactions. The demo uses Hadoop Hive and MapReduce to schematize, refine and transform raw Twitter data. It will also focuses on the Hive endpoint that HDInsight exposes for client applications to consume HDInsight data through the Hive ODBC interface. Finally, this session will show the present day self-service BI tools (Power View, Power Query and Power Map) to demonstrate how you can generate powerful and interactive visualization on your twitter data to enhance your brand promotion/productivity with just a few mouse clicks.
Views: 35414 Debarchan Sarkar
In this video I process transcriptions from Hugo Chavez's TV programme "Alo Presidente" to find patterns in his speech. Watching this video you will learn how to: -Download several documents at once from a webpage using a Firefox plugin. - Batch convert pdf files to text using a very simple script and a java application. - Process documents with Rapid Miner using their association rules feature to find patterns in them.
Views: 35512 Alba Madriz
DATA MINING It is the process to discover the knowledge or hidden pattern form large databases. The overall goal of data mining is to extract and obtain information from databases and transfer it into an understandable format for use in future. It is used by Business intelligence organizations, Financial analysts, Marketing organizations, and companies with a strong consumer focus like retail ,financial and communication . DATA MINING (cont.): It can also be seen as one of the core process of knowledge discovery in data base (KDD). It can be viewed as process of Knowledge Discovery in database. Data Extraction/gathering:- To collect the data from sources . Eg: data warehousing. Data cleansing :- To eliminate bogus data and errors. Feature extraction:- To extract only task relevant data : i.e to obtain the interesting attributes of data . Pattern extraction and discovery :- This step is seen as process of data mining , where one should concentrate the effort. Visualization of the data and Evaluation of results :- To create knowledge base. CLASSIFICATION Classification is a technique of data mining to classify each item into predefined set of groups or classes. The goal of classification is to accurately predict the target class for each item in the data. For example, a classification model could be used to identify loan applicants as low, medium, or high credit risks. The simplest type of classification problem is binary classification. In binary classification, the target attribute has only two possible values: for example, high credit rating or low credit rating. Multiclass targets have more than two values: for example, low, medium, high, or unknown credit rating. SENTIMENT ANALYSIS Sentiment analysis is a sub-domain of opinion mining where the analysis is focused on the extraction of emotions and opinions of the people towards a particular topic. Sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic. The attitude may be his or her judgment or evaluation, affective state (that is to say, the emotional state of the author when writing), or the intended emotional communication (that is to say, the emotional effect the author wishes to have on the reader). With opinion mining, we can distinguish poor content from high quality content. For more information and query visit our website: Website : http://www.e2matrix.com Blog : http://www.e2matrix.com/blog/ WordPress : https://teche2matrix.wordpress.com/ Blogger : https://teche2matrix.blogspot.in/ Contact Us : +91 9041262727 Follow Us on Social Media Facebook : https://www.facebook.com/etwomatrix.researchlab Twitter : https://twitter.com/E2MATRIX1 LinkedIn : https://www.linkedin.com/in/e2matrix-training-research Google Plus : https://plus.google.com/u/0/+E2MatrixJalandhar Pinterest : https://in.pinterest.com/e2matrixresearchlab/ Tumblr : https://www.tumblr.com/blog/e2matrix24
Views: 358 E2MATRIX RESEARCH LAB
http://blogs.ischool.berkeley.edu/i290-abdt-s12/ Lecture 6: Kostas Tsioutsiouliklis on Twitter Trends and how to compute them Lecture notes: http://blogs.ischool.berkeley.edu/i290-abdt-s12/files/2012/08/Kostas_Trends_Sept_13_2012.pdf Course: Information 290. Analyzing Big Data with Twitter School of Information UC Berkeley Prof. Marti Hearst Course description: How to store, process, analyze and make sense of Big Data is of increasing interest and importance to technology companies, a wide range of industries, and academic institutions. In this course, UC Berkeley professors and Twitter engineers will lecture on the most cutting-edge algorithms and software tools for data analytics as applied to Twitter microblog data. Topics will include applied natural language processing algorithms such as sentiment analysis, large scale anomaly detection, real-time search, information diffusion and outbreak detection, trend detection in social streams, recommendation algorithms, and advanced frameworks for distributed computing. Social science perspectives on analyzing social media will also be covered. This is a hands-on project course in which students are expected to form teams to complete intensive programming and analytics projects using the real-world example of Twitter data and code bases. Engineers from Twitter will help advise student projects, and students will have the option of presenting their final project presentations to an audience of engineers at the headquarters of Twitter in San Francisco (in addition to on campus). Project topics include building on existing infrastructure tools, building Twitter apps, and analyzing Twitter data. Access to data will be provided.
Views: 16341 Berkeley School of Information
Sentiment Analysis Systems for Turkish Tweets, Oğuzhan Karaçakır, Bünyamin İnce Boğaziçi University Computer Engineering 02.06.2016 BS graduation project The project analyzes Tweets according to their senses such as positive, negative or neutral. More info can be found in report : http://www.megafileupload.com/fgbG/Report_Of_Project.pdf Bogazici University, Department of Computer Engineering, Graduation Project, Spring 2016
Views: 59 Oğuzhan Karaçakır
Download the PDF to keep as reference http://theexcelclub.com/extract-key-phrases-from-text/ FREE Power BI course - Power BI - The Ultimate Orientation http://theexcelclub.com/free-excel-training/ Or on Udemy https://www.udemy.com/power-bi-the-ultimate-orientation Or on Android App https://play.google.com/store/apps/details?id=com.PBI.trainigapp Carry out a text analytics like the big brand...only for free with Power BI and Microsoft Cognitive Services. this video will cover Obtain a Text Analytics API Key from Microsoft Cognitive Services Power BI – Setting up the Text Data Setting up the Parameter in Power BI Setting up the Custom function Query(with code to copy) Grouping the text Running the Key Phrase Extraction by calling the custom function. Extracting the key phrases from the returned Json file. Sign up to our newsletter http://theexcelclub.com/newsletter/ Watch more Power BI videos https://www.youtube.com/playlist?list=PLJ35EHVzCuiEsQ-68y0tdnaU9hCqjJ5Dh Watch Excel Videos https://www.youtube.com/playlist?list=PLJ35EHVzCuiFFpjWeK7CE3AEXy_IRZp4y Join the online Excel and PowerBI community https://plus.google.com/u/0/communities/110804786414261269900
Views: 4534 Paula Guilfoyle
Sentiment analysis of your customer could be a very important part in business analysis. Watch and learn how to do sentiment analysis. If you want to learn more analysis in Power BI enroll in this course at 92% off - https://www.udemy.com/microsoft-power-bi-a-complete-hands-on-training-new-updates/?couponCode=BIPOWER Also, get a personal 1-to-1 assistant during your Power BI learning.
Views: 92 Deepesh Vashistha
Talk presented at the Mining Software Repositories (MSR Mining Challenge session) in Buenos Aires, Argentina. Paper: https://rodrigorgs.github.io/files/msr2017-rodrigo.pdf Slides: https://speakerdeck.com/rodrigorgs/sentiment-analysis-of-travis-ci-builds
Views: 157 Rodrigo Rocha Gomes e Souza
SAS Technical Consultant Jenn Sykes about her use of SAS Sentiment Analysis and SAS Forecast Studio to predict outcomes of popular elections. To learn more , read the paper "Predicting Electoral Outcomes with SAS ® Sentiment Analysis and SAS ® Forecast Studio " at http://support.sas.com/resources/papers/proceedings12/131-2012.pdf To learn more about SAS Sentiment Analysis, visit http://www.sas.com/text-analytics/sentiment-analysis/
Views: 1197 SAS Software
Does using social media to gauge sentiment accurately reflect stock price movement? See more options trading videos: http://ow.ly/ODhHs On today's Skinny on Options Data Science, Tom and Tony are joined by Mike Rechenthin, PhD (Dr. Data) to discuss how some traders are using sentiment analysis to learn the opinions about specific stocks of thousands of people on social media websites (i.e. Twitter, Facebook, and Yahoo Finance message boards). Dr. Data first explains the algorithm behind sentiment analysis; then describes some of its shortcomings and how the number can be manipulated. While he believes it has useful applications in fields like marketing, he doubts its usefulness for predicting the opinions of investors. Instead of using sentiment analysis to determine a positive or negative opinion of a stock, he suggests relying more on another measure of investor sentiment, an option’s implied volatility. Math is the most feared four-lettered word around, even to Tom and Tony. Luckily the well dressed Dr. Data is here to show how to tame the beast and even use it to make money. Check out his segments on analysis and data manipulation to understand the reasoning behind our trades. You can watch a new Skinny on Options Data Science episode live and check out all previous episodes everyday at http://ow.ly/EoyGW! ======== tastytrade.com ======== Finally a financial network for traders, built by traders. Hosted by Tom Sosnoff and Tony Battista, tastytrade is a real financial network with 8 hours of live programming five days a week during market hours. From pop culture to advanced investment strategies, tastytrade has a broad spectrum of content for viewers of all kinds! Tune in and learn how to trade options successfully and make the most of your investments! Plus, access our visual trading platform, dough, to learn the basics of options trading and manage your portfolio! With hours of tutorial videos and unique tools on a simple, easy-to-use trading interface, dough.com is here to make learning how to trade options fun! Subscribe to our YouTube channel: http://goo.gl/s2bAxF Watch tastytrade LIVE daily Monday-Friday 7am-3:15pmCT: https://goo.gl/OTv3Ez Follow tastytrade: Twitter: https://twitter.com/tastytrade Facebook: https://www.facebook.com/tastytrade LinkedIn: http://www.linkedin.com/company/tastytrade Instagram: http://instagram.com/tastytrade Pinterest: http://www.pinterest.com/tastytrade/
Views: 12171 tastytrade
Mehr Infos im Blog: https://www.maibornwolff.de/blog/aspekt-basierte-sentiment-analyse Die Folien des Vortrags gibt es hier zum Download: http://download.maibornwolff.de/Joint_Aspect_and_Sentiment_Analysis.pdf
Views: 48 MaibornWolff GmbH
There has been a meteoric rise in the amount of multilingual content on the web. This is primarily due to social media sites such as Facebook, and Twitter, as well as blogs, discussion forums, and reader responses to articles on traditional news sites. Language usage statistics indicate that Chinese is a very close second to English, and could overtake it to become the dominant language on the web. It is also interesting to see the explosive growth in languages such as Arabic. The availability of this content warrants a discussion on how such information can be effectively utilized. Such data can be mined for many purposes including business-related competitive insight, e-commerce, as well as citizen response to current issues. This talk will begin with motivations for multilingual text mining, including commercial and societal applications, digital humanities applications such as semi-automated curation of online discussion forums, and lastly, government applications, where the value proposition (benefits, costs and value) is different, but equally compelling. There are several issues to be touched upon, beginning with the need for processing native language, as opposed to using machine translated text. In tasks such as sentiment or behaviour analysis, it can certainly be argued that a lot is lost in translation, since these depend on subtle nuances in language usage. On the other hand, processing native language is challenging, since it requires a multitude of linguistic resources such as lexicons, grammars, translation dictionaries, and annotated data. This is especially true for "resourceMpoor languages" such as Urdu, and Somali, languages spoken in parts of the world where there is considerable focus nowadays. The availability of content such as multilingual Wikipedia provides an opportunity to automatically generate needed resources, and explore alternate techniques for language processing. The rise of multilingual social media also leads to interesting developments such as code mixing, and code switching giving birth to "new" languages such as Hinglish, Urdish and Spanglish! This phenomena exhibits both pros and cons, in addition to posing difficult challenges to automatic natural language processing. But there is also an opportunity to use crowd-sourcing to preserve languages and dialects that are gradually becoming extinct. It is worthwhile to explore frameworks for facilitating such efforts, which are currently very ad hoc. In summary, the availability of multilingual data provides new opportunities in a variety of applications, and effective mining could lead to better cross-cultural communication. Questions Addressed (i) Motivation for mining multilingual text. (ii) The need for processing native language (vs. machine translated text). (iii) Multilingual Social Media: challenges and opportunities, e.g., preserving languages and dialects.
Views: 1440 UA German Department
Sentiment data shows us how traders are positioning themselves in the market. In this video we discuss how to interpret IG Client sentiment data, and then work it into an active trading strategy. For a real time look at sentiment figures go to: https://goo.gl/2Dk7LO
Views: 5210 DailyFX
Welcome to Module 4E of the "YTC - Introduction to Trading - Technical Analysis" video series. This series is designed to provide you with a complete introductory-level education in the field of Technical Analysis. ***NOTE: See here for copies of workbooks for this video module. Plus an explanation as to the current state of this "unfinished" video series. http://yourtradingcoach.com/trading-process-and-strategy/ytc-intro-to-ta-unfinished-video-series/ Contents: Module 1 - Introduction - Introduction to Series - Aim of Series Module 2 - Market Analysis - Aims of Analysis - Methods of Analysis - Technical Analysis - Fundamental Analysis - Other Module 3 - Technical Analysis Tools & Methods - What are Charts? - Chart Components - Price Display - Multiple Timeframe Relationship - Technical Analysis Methods - Chart Analysis - Indicator Analysis - Volume Analysis - Other Module 4 - Chart Analysis - Market Environment - Swing High/Low Structure - Trends and Ranges - Trendlines and Channels - Support and Resistance - Charting Patterns - Macro - Micro Module 5 - Indicator Based Analysis - Classes of Indicator - Trend Indicators - Momentum Indicators - Volatility Indicators - Other Module 6 - Volume Analysis - Principles Module 7 - Other Forms of Analysis - Pivot Points / Fibonacci Analysis - Market Profile - Cycle Analysis - Market Internals - Inter-Market Analysis - Sentiment Analysis Module 8 - Multiple Timeframe Analysis - How Multiple Timeframes Combine Module 9 - Trading with Technical Analysis - Trading Methods - Mechanical vs Discretionary - Discretionary Trading Setups - With-Trend - Counter-Trend - Choosing Setups Appropriate to the Environment Module 10 - Your Trading Strategy - Defining and Documenting your Strategy - Strategy Testing - Trader Development Module 11 - An Introduction to Advanced Technical Analysis - Introduction to the Advanced Technical Analysis topics to be discussed through video or webinar at www.YourTradingCoach.com Website: http://www.YourTradingCoach.com See here to download Workbooks associated with these videos: http://www.yourtradingcoach.com/Videos-Technical-Analysis/YTC-Introduction-to-Technical-Analysis.html
Views: 20775 YourTradingCoach
Sentiment Analysis Implementation Find the terms here: http://ptrckprry.com/course/ssd/data/positive-words.txt http://ptrckprry.com/course/ssd/data/negative-words.txt
Views: 6304 Jalayer Academy
Uploaded on the 167th anniversary of the Seneca Falls Convention, we take a look at the big idea of women's rights in the 19th century as well as some interesting facts about the convention. Read the Declaration of Sentiments http://www.womensrightsfriends.org/pdfs/1848_declaration_of_sentiments.pdf Visit HipHughes Online www.hiphughes.com
Views: 30723 Hip Hughes
Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 5: Classifying tweets http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3902 WekaMOOC
At the Tactical Investor http://tacticalinvestor.com, our focus is not only on Technical analysis, for technical analysis spots the symptoms of the disease, but it does not identify the cause. The trend indicator blends the best elements of Technical Analysis with the powerful field of Mass Psychology. Our investment philosophy is very simple, identify the trend and stick with until it ends.
Views: 6544 Sol Tactical
This is a example in GATE which shows the results of the default ANNIE pipeline on an English document. In this case the document is "That's what she said" that lovely catch phrase from Michael Scott in The Office TV show http://www.cs.washington.edu/homes/brun/pubs/pubs/Kiddon11.pdf it discusses humor recognition...
Views: 29813 cesine0
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Views: 855 IBM Analytics Learning Services
Learn more about text mining: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Hi, I'm Ted. I'm the instructor for this intro text mining course. Let's kick things off by defining text mining and quickly covering two text mining approaches. Academic text mining definitions are long, but I prefer a more practical approach. So text mining is simply the process of distilling actionable insights from text. Here we have a satellite image of San Diego overlaid with social media pictures and traffic information for the roads. It is simply too much information to help you navigate around town. This is like a bunch of text that you couldn’t possibly read and organize quickly, like a million tweets or the entire works of Shakespeare. You’re drinking from a firehose! So in this example if you need directions to get around San Diego, you need to reduce the information in the map. Text mining works in the same way. You can text mine a bunch of tweets or of all of Shakespeare to reduce the information just like this map. Reducing the information helps you navigate and draw out the important features. This is a text mining workflow. After defining your problem statement you transition from an unorganized state to an organized state, finally reaching an insight. In chapter 4, you'll use this in a case study comparing google and amazon. The text mining workflow can be broken up into 6 distinct components. Each step is important and helps to ensure you have a smooth transition from an unorganized state to an organized state. This helps you stay organized and increases your chances of a meaningful output. The first step involves problem definition. This lays the foundation for your text mining project. Next is defining the text you will use as your data. As with any analytical project it is important to understand the medium and data integrity because these can effect outcomes. Next you organize the text, maybe by author or chronologically. Step 4 is feature extraction. This can be calculating sentiment or in our case extracting word tokens into various matrices. Step 5 is to perform some analysis. This course will help show you some basic analytical methods that can be applied to text. Lastly, step 6 is the one in which you hopefully answer your problem questions, reach an insight or conclusion, or in the case of predictive modeling produce an output. Now let’s learn about two approaches to text mining. The first is semantic parsing based on word syntax. In semantic parsing you care about word type and order. This method creates a lot of features to study. For example a single word can be tagged as part of a sentence, then a noun and also a proper noun or named entity. So that single word has three features associated with it. This effect makes semantic parsing "feature rich". To do the tagging, semantic parsing follows a tree structure to continually break up the text. In contrast, the bag of words method doesn’t care about word type or order. Here, words are just attributes of the document. In this example we parse the sentence "Steph Curry missed a tough shot". In the semantic example you see how words are broken down from the sentence, to noun and verb phrases and ultimately into unique attributes. Bag of words treats each term as just a single token in the sentence no matter the type or order. For this introductory course, we’ll focus on bag of words, but will cover more advanced methods in later courses! Let’s get a quick taste of text mining!
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